What is the best way to use the architecture of defined models from tf.keras.applications for non-image dataset?

Question

I'm trying to use models from tf.keras.applications such as VGG16 for my non-image data for my sequential classification task.

My X_train input shape = (# samples, window size, # columns) Number of classes = 2

What would be the best way to copy architecture of the model and modify parameter details such as input shapes for input/hidden/output layers?

Thanks!

Answer 1

If you are looking for a quick way to find and modify the code that defines the architecture of VGG16 then looking at the source code of Keras would be the easiest one:

# Block 1
x = layers.Conv2D(64, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block1_conv1')(img_input)
x = layers.Conv2D(64, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block1_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

# Block 2
x = layers.Conv2D(128, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block2_conv1')(x)
x = layers.Conv2D(128, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block2_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)

# Block 3
x = layers.Conv2D(256, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block3_conv1')(x)
x = layers.Conv2D(256, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block3_conv2')(x)
x = layers.Conv2D(256, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block3_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)

# Block 4
x = layers.Conv2D(512, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block4_conv1')(x)
x = layers.Conv2D(512, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block4_conv2')(x)
x = layers.Conv2D(512, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block4_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)

# Block 5
x = layers.Conv2D(512, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block5_conv1')(x)
x = layers.Conv2D(512, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block5_conv2')(x)
x = layers.Conv2D(512, (3, 3),
                  activation='relu',
                  padding='same',
                  name='block5_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)

if include_top:
    # Classification block
    x = layers.Flatten(name='flatten')(x)
    x = layers.Dense(4096, activation='relu', name='fc1')(x)
    x = layers.Dense(4096, activation='relu', name='fc2')(x)
    x = layers.Dense(classes, activation='softmax', name='predictions')(x)
else:
    if pooling == 'avg':
        x = layers.GlobalAveragePooling2D()(x)
    elif pooling == 'max':
        x = layers.GlobalMaxPooling2D()(x)

# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
    inputs = keras_utils.get_source_inputs(input_tensor)
else:
    inputs = img_input
# Create model.
model = models.Model(inputs, x, name='vgg16')